Example Notebook (Risk Factors for Cervical Cancer)

Load a dataset

In this notebook a dataset named 'Risk Factors for Cervical Cancer'. The dataset was collected at 'Hospital Universitario de Caracas' in Caracas, Venezuela. The dataset comprises demographic information, habits, and historic medical records of 858 patients. Several patients decided not to answer some of the questions because of privacy concerns (missing values).

Preprocess the dataset

The dataset will be used same as described here: https://christophm.github.io/interpretable-ml-book/cervical.html All unknown values (\?) are going to be set to 0.0.

Visualize the dataset

Three visualization functions offered by the XAI module will be used for analyzing the dataset.

Target

In the cell below the target variable is selected. The Biopsy serves as the gold standard for diagnosing cervical cancer, therefore we will use it as target.

Training the models

Four models are going to be trained on this dataset. In the output below we can see accuracy, classification reports, confusion matrix and ROC Curve for each model.

Global model interpretations

In the following steps we will use global interpretation techniques that help us to answer questions like how does a model behave in general? What features drive predictions and what features are completely useless. This data may be very important in understanding the model better. Most of the techniques work by investigating the conditional interactions between the target variable and the features on the complete dataset.

Feature importance

The importance of a feature is the increase in the prediction error of the model after we permuted the feature’s values, which breaks the relationship between the feature and the true outcome. A feature is “important” if permuting it increases the model error. This is because in that case, the model relied heavily on this feature for making right prediction. On the other hand, a feature is “unimportant” if permuting it doesn’t affect the error by much or doesn’t change it at all.

ELI5

In the first case, we use ELI5, which does not permute the features but only visualizes the weight of each feature.

Skater

In this step we use the Skater module, which permutes the features to generate a feature importance plot.

Shap

In the cell below we use the SHAP (SHapley Additive exPlanations). It uses a combination of feature contributions and game theory to come up with SHAP values. Then, it computes the global feature importance by taking the average of the SHAP value magnitudes across the dataset.

Partial Dependence Plots

The partial dependence plot (short PDP or PD plot) shows the marginal effect one or two features have on the predicted outcome of a machine learning model. A partial dependence plot can show whether the relationship between the target and a feature is linear, monotonic or more complex. For example, when applied to a linear regression model, partial dependence plots always show a linear relationship.

PDPBox

PDPBox is the first module that we use for ploting partial dependence.

Skater

SHAP

Local model interpretations

Local interpretation focuses on specifics of each individual and provides explanations that can lead to a better understanding of the feature contribution in smaller groups of individuals that are often overlooked by the global interpretation techniques. We will use two moduels for interpreting single instances - SHAP and LIME.

SHAP

SHAP leverages the idea of Shapley values for model feature influence scoring. The technical definition of a Shapley value is the “average marginal contribution of a feature value over all possible coalitions.” In other words, Shapley values consider all possible predictions for an instance using all possible combinations of inputs. Because of this exhaustive approach, SHAP can guarantee properties like consistency and local accuracy. LIME, on the other hand, does not offer such guarantees.

LIME

LIME (Local Interpretable Model-agnostic Explanations) builds sparse linear models around each prediction to explain how the black box model works in that local vicinity. While treating the model as a black box, we perturb the instance we want to explain and learn a sparse linear model around it, as an explanation. LIME has the advantage over SHAP, that it is a lot faster.

Example 1
Example 2
Example 3
Example 4